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The uncertainty of target sizes and the complexity of backgrounds are the main reasons for the poor detection performance of small infrared targets. Focusing on this issue, this paper presents a robust and accurate algorithm that combines multiscale kurtosis map fusion and the optical flow method for the detection of small infrared targets in complex natural scenes. The paper has made three main contributions: First, it proposes a structure for infrared small target detection technology based on multiscale kurtosis maps and optical flow fields, which can well represent the shape, size and motion information of the target and is advantageous to the enhancement of the target and the suppression of the background. Second, a strategy of multi-scale kurtosis map fusion is presented to match the shape and the size of the small target, which can effectively enhance small targets with different sizes as well as suppress the highlighted noise points and the residual background edges. During the fusion process, a novel weighting mechanism is proposed to fuse different scale kurtosis maps, by means of which the scale that matches the true target is effectively enhanced. Third, an improved optical flow method is utilized to further suppress the nontarget residual clutter that cannot be completely removed by multiscale kurtosis map fusion. By means of the scale confidence parameter obtained during the multiscale kurtosis map fusion step, the optical flow method can select the optimal neighborhood that matches best to the target size and shape, which can effectively improve the integrity of the detection target and the ability to suppress residual clutter. As a result, the proposed method achieves a superior performance. Experimental results on eleven typical complex infrared natural scenes show that, compared with seven state-of-the-art methods, the presented method outperforms in terms of subjective visual effect, as well as some main objective evaluation indicators such as BSF, SCRG and ROC, etc.

Automatic Digital Orthophoto Map (DOM) generation plays an important role in many downstream works such as land use and cover detection, urban planning, and disaster assessment. Existing DOM generation methods can generate promising results but always need ground object filtered DEM generation before otho-rectification; this can consume much time and produce building facade contained results. To address this problem, a pixel-by-pixel digital differential rectification-based automatic DOM generation method is proposed in this paper. Firstly, 3D point clouds with texture are generated by dense image matching based on an optical flow field for a stereo pair of images, respectively. Then, the grayscale of the digital differential rectification image is extracted directly from the point clouds element by element according to the nearest neighbor method for matched points. Subsequently, the elevation is repaired grid-by-grid using the multi-layer Locally Refined B-spline (LR-B) interpolation method with triangular mesh constraint for the point clouds void area, and the grayscale is obtained by the indirect scheme of digital differential rectification to generate the pixel-by-pixel digital differentially rectified image of a single image slice. Finally, a seamline network is automatically searched using a disparity map optimization algorithm, and DOM is smartly mosaicked. The qualitative and quantitative experimental results on three datasets were produced and evaluated, which confirmed the feasibility of the proposed method, and the DOM accuracy can reach 1 Ground Sample Distance (GSD) level. The comparison experiment with the state-of-the-art commercial softwares showed that the proposed method generated DOM has a better visual effect on building boundaries and roof completeness with comparable accuracy and computational efficiency.

The development of an efficient and robust method for dense image-matching has been a technical challenge due to high variations in illumination and ground features of aerial images of large areas. In this paper, we propose a method for the dense matching of aerial images using an optical flow field and a fast-guided filter. The proposed method utilizes a coarse-to-fine matching strategy for a pixel-wise correspondence search across stereo image pairs. The pyramid Lucas–Kanade (L–K) method is first used to generate a sparse optical flow field within the stereo image pairs, and an adjusted control lattice is then used to derive the multi-level B-spline interpolating function for estimating the dense optical flow field. The dense correspondence is subsequently refined through a combination of a novel cross-region-based voting process and fast guided filtering. The performance of the proposed method was evaluated on three bases, namely, the matching accuracy, the matching success rate, and the matching efficiency. The evaluative experiments were performed using sets of unmanned aerial vehicle (UAV) images and aerial digital mapping camera (DMC) images. The results showed that the proposed method afforded the root mean square error (RMSE) of the reprojection errors better than ±0.5 pixels in image, and a height accuracy within ±2.5 GSD (ground sampling distance) from the ground. The method was further compared with the state-of-the-art commercial software SURE and confirmed to deliver more complete matches for images with poor-texture areas, the matching success rate of the proposed method is higher than 97% while SURE is 96%, and there is 47% higher matching efficiency. This demonstrates the superior applicability of the proposed method to aerial image-based dense matching with poor texture regions.

With advancements in deep learning and image processing technology, evaluations of lower limb rehabilitation training have witnessed improved accuracy and efficiency. However, traditional image processing techniques frequently neglect the inherent sequence-level characteristics during action recognition and fail to exploit the comprehensive full-field data when discerning rehabilitation training action standards. Addressing these limitations, a novel method was proposed. Initially, the GaitSet algorithm was employed to recognize the lower limb rehabilitation training actions, ensuring complete consideration of sequence-level features. Subsequently, leveraging the full-field optical flow tracking approach, challenges associated with discerning the standards of lower limb rehabilitation training actions were examined. It is anticipated that this novel methodology can offer an enhanced tool for evaluating the effectiveness of lower limb rehabilitation training in the realm of rehabilitation medicine. Such advancements could potentially contribute to optimizing rehabilitation outcomes and augmenting patients' quality of life.

This paper proposes a novel particle image velocimetry (PIV) technique to generate an instantaneous two-dimensional velocity field for sediment-laden fluid based on the optical flow algorithm of ultrasound imaging. In this paper, an ultrasonic PIV (UIV) system is constructed by integrating a medical ultrasound instrument and an ultrasonic particle image velocimetry algorithm. The medical ultrasound instrument with a phased sensor array is used to acquire acoustic echo signals of particles in water and generate two-dimensional underwater ultrasound images. Based on the optical flow field, the instantaneous velocity of the particles in water corresponding to the pixels in the ultrasonic particle images is derived from the grayscale change between adjacent images under the L-K local constraint based on the optical flow field, and finally, the two-dimensional flow field is obtained. Through multiple sets of experiments, the proposed algorithm is verified. The experimental results are compared with those of the conventional cross-correlation algorithms. The results show that the L-K optical flow method can not only obtain the underwater velocity field accurately, but also has the advantages of good smoothness and extensive suitability, especially for the flow field measurement in sediment-laden fluid than conventional algorithms.

Many applications require action recognition skills, from human-machine interaction to intelligent video surveillance. Action recognition in video sequences cannot be based on simply processing raw color images or optical flow fields. Color images provide appearance information of moving objects, but lack motion features. They are also very sensitive to variations due to clothing and camera pose that badly affect the action recognition accuracy. In turn, raw optical flow measures instantaneous motion, not the overall dynamics of actions, and is sensitive to noise. More robust and meaningful motion features and classifiers are thus required for action recognition to be reliable. This paper proposes a new action recognition technique based on a deep convolutional neural network (CNN) fed with Histograms of Optical Flow Co-Occurrence (HOF-CO) motion features. HOF-CO is a robust motion representation previously proposed by the authors to encode the relative frequency of pairs of optical flow directions computed at each image pixel. Experimental results show that this approach outperforms state-of-the-art action recognition methods on three different public datasets KTH, UCF-11 Youtube and HOLLYWOOD2.

A fully automatic initialization approach for 3D-model-based vehicle tracking has been developed, based on Edge-Element and Optical-Flow association. An entire automatic initialization and tracking system incorporating this approach achieves results comparable to those obtained by earlier experiments based on semi-interactive initialization, provided the assessment criteria are roughly equivalent. Experiences with a large testing sample—about 15 minutes of inner-city traffic videos—are discussed in detail.

This paper proposes a stable method of generating a stereoscopic panoramic video with the omnidirectional stereo (ODS) format. Different from the traditional image stitching method which can only be applied to generate a monocular panorama, we adopt an optical flow-based blending method to create two panoramas for binocular vision. In addition, traditional image stitching methods based on seam-finding tend to cause the problem of temporal flicker. We address this problem by restricting the optical flow field of the new frame with its previous frame's optical flow field. Thus, the generated video is stable and out of temporal flicker. There are four key operations in our approach. First, we adopt the ODS format which is the basis of stereoscopic panorama. Second, we do effective exposure compensation, making the brightness of the two eyes' panoramas consistent. Third, we employ an optical flow-based blending method to synthesis the final panorama effectively. Fourth, we take the previous frame's optical flow field as the restriction of the present frame's optical flow field to acquire a stable video. The final output videos can deliver a pleasant and impressive stereoscopic viewing experience to the audience when the audience watches the videos in the virtual reality headset.

P237; 给出了航摄影像密集匹配的总体流程,依据是否显式使用光滑假设将密集匹配方法分为局部最优密集匹配和全局最优密集匹配两类,深入探讨了两种方法的关键技术,指出了从理论、技术、普适性和实用性方面值得关注的问题,期望能对相关研究有所裨益.

Based on video frame differential optical flow field, a method of crucial area detection for surveillance video images of examination room is proposed in this paper. Firstly, the optical flow field was calculated with the difference between two adjacent frames. Secondly, the scene was divided roughly into several blocks, and the blocks of which centroid speed is higher than given threshold were further divided into fine sub-blocks, and furthermore, the sub-block which has maximum centroid speed in the block was marked as the area of abnormal target. Finally, the sub-blocks with exceptional speed in the same observation time slice were judged to be the correlate areas with abnormal speed (CAAS), and the intersection of adjacent CAAS were determined as the crucial area. Experimental results show that the proposed method can effectively detect the abnormal movement area, and can accurately position the crucial area affecting other targets movement.